# coding=utf-8
# author=yphacker

import torch
import torch.nn.functional as F
from torch.autograd import Variable
import torch.nn as nn


# 权重初始化，默认xavier
def init_network(model, method='xavier', exclude='embedding', seed=0):
    for name, w in model.named_parameters():
        if exclude not in name:
            if 'weight' in name:
                if method == 'xavier':
                    nn.init.xavier_normal_(w)
                elif method == 'kaiming':
                    nn.init.kaiming_normal_(w)
                else:
                    nn.init.normal_(w)
            elif 'bias' in name:
                nn.init.constant_(w, 0)
            else:
                pass


class FocalLoss(nn.Module):
    def __init__(self, gamma=0, alpha=None, size_average=True):
        super(FocalLoss, self).__init__()
        self.gamma = gamma
        self.alpha = alpha
        if isinstance(alpha, (float, int)): self.alpha = torch.Tensor([alpha, 1 - alpha])
        if isinstance(alpha, list): self.alpha = torch.Tensor(alpha)
        self.size_average = size_average

    def forward(self, input, target):
        if input.dim() > 2:
            input = input.view(input.size(0), input.size(1), -1)  # N,C,H,W => N,C,H*W
            input = input.transpose(1, 2)  # N,C,H*W => N,H*W,C
            input = input.contiguous().view(-1, input.size(2))  # N,H*W,C => N*H*W,C
        target = target.view(-1, 1)
        logpt = F.log_softmax(input, dim=1)
        # logpt = F.log_softmax(input)
        logpt = logpt.gather(1, target)
        logpt = logpt.view(-1)
        pt = Variable(logpt.data.exp())

        if self.alpha is not None:
            if self.alpha.type() != input.data.type():
                self.alpha = self.alpha.type_as(input.data)
            at = self.alpha.gather(0, target.data.view(-1))
            logpt = logpt * Variable(at)

        loss = -1 * (1 - pt) ** self.gamma * logpt
        if self.size_average:
            return loss.mean()
        else:
            return loss.sum()


def get_score(x):
    return 1.0 / (1.0 + x)
